Question

当我尝试将模型用作图层时，我在Keras中遇到异常。我的代码如下：

from keras import layers
from keras import applications
from keras import Input
from keras.models import Model


xception_base = applications.Xception(weights=None,
                                      include_top=False)

left_input = Input(shape=(250, 250, 3))
right_input = Input(shape=(250, 250, 3))

left_features = xception_base(left_input)
right_input = xception_base(right_input)

merged_features = layers.concatenate([left_features, right_input], axis=-1)
model = Model([left_input, right_input], merged_features)

这是我得到的例外。从异常中我不清楚是怎么回事

Traceback (most recent call last):
  File "/home/asattar/workspace/projects/keras-examples/chapter7/MergeTwoModels.py", line 18, in <module>
    model = Model([left_input, right_input], merged_features)
  File "/usr/local/lib/python2.7/dist-packages/Keras-2.2.4-py2.7.egg/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/Keras-2.2.4-py2.7.egg/keras/engine/network.py", line 93, in __init__
    self._init_graph_network(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/Keras-2.2.4-py2.7.egg/keras/engine/network.py", line 224, in _init_graph_network
    assert node_index == 0
AssertionError

有人可以帮助我解决可能出现的问题吗？

当我这样做时也没有错误

model = Model(left_input, left_features)

Answer 1

实际上是nvm。我意识到我弄乱了变量名

Confusion Matrix
[[12357  2936  6039]
 [ 1479  4301  1927]
 [ 3316  1924 12495]]
Classification Report
             precision    recall  f1-score   support

   class -1       0.72      0.58      0.64     21332
    class 0       0.47      0.56      0.51      7707
    class 1       0.61      0.70      0.65     17735

avg / total       0.64      0.62      0.62     46774

使图形变为圆形。

尝试将XCeption模型用作Keras中的图层时Keras中的异常

1 个答案: